Research

We develop causal machine learning and AI methods that integrate modern AI models with principled causal reasoning. Our work focuses on estimating heterogeneous treatment effects, addressing systematic bias in observational data, and improving robustness and interpretability of causal models in real-world settings. We study how causal structure, negative controls, and representation learning can be incorporated into machine learning pipelines to support reliable decision making, particularly in high-dimensional and complex healthcare data.